Vehicle Accident and Traffic Classification Using Deep Convolutional Neural Networks

Kumeda, Bulbula; Zhang, Fengli; Oluwasanmi, Ariyo; Owusu, Forster; Assefa, Maregu; Amenu, Temesgen

doi:10.1109/iccwamtip47768.2019.9067530

Cited by 18 publications

(5 citation statements)

References 15 publications

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“…As the field progressed, the introduction of image-based detection marked a significant breakthrough. Research [15] delved into deep learning, employing Convolutional Neural Networks (CNNs) to categorize traffic and accident photos. This shift towards utilizing external surveillance data expanded the potential applications beyond individual vehicles.…”

Section: Literature Surveymentioning

confidence: 99%

Vision Transformer-Based Multi-Phase Accident Detection and False Positive Mitigation

Jartarghar,

Sanghvi,

Nidgundi

et al. 2024

Preprint

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The use of computer vision algorithms for real-time accident detection is a highly researched method to minimize delays in post-crash care. Various methods are adopted in the literature; However, based on our understanding, these methods are not sufficiently reliable in accurately detecting accidents, and there is a high occurrence of false accident detections. This paper aims to achieve precise accident detection while minimizing false positive detections. Our approach involves a four-phase framework that integrates vehicle detection and continuous tracking techniques, specifically utilizing the You Only Look Once (YOLO) and ByteTrack algorithms. Subsequently, we have developed a criterion for identifying abrupt change involving checking for overlap between vehicles and angle of collision, a potential accident indicator. During our third phase, we precisely determine the location of the accident within the sudden alteration frames. The fourth pivotal phase stands out, leveraging Vision Transformer (ViT), an encoder only model, to carefully eliminate fake accidents. Our methodology surpasses the typical use of Convolutional Neural Network (CNN)-based approaches by demonstrating a comprehensive integration of several deep learning techniques. The framework was evaluated on Real-World Surveillance videos with diverse conditions; The performance of proposed framework was found effective, outperforming the existing works.

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Section: Literature Surveymentioning

confidence: 99%

Vision Transformer-Based Multi-Phase Accident Detection and False Positive Mitigation

Jartarghar,

Sanghvi,

Nidgundi

et al. 2024

Preprint

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“…The study provides a reference and benchmark model for traffic scene classification using deep learning methods. Bulbula Kumeda et al [42] utilized a CNN network consisting of four convolutional layers and three fully connected layers for image classification on the Traffic-Net dataset. They achieved an impressive accuracy of 94.4% across four image categories: dense traffic, sparse traffic, fire, and traffic accidents.…”

Section: Related Workmentioning

confidence: 99%

Res2Net-based multi-scale and multi-attention model for traffic scene image classification

Gao,

Guo,

Zhou

et al. 2024

PLoS ONE

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With the increasing applications of traffic scene image classification in intelligent transportation systems, there is a growing demand for improved accuracy and robustness in this classification task. However, due to weather conditions, time, lighting variations, and annotation costs, traditional deep learning methods still have limitations in extracting complex traffic scene features and achieving higher recognition accuracy. The previous classification methods for traffic scene images had gaps in multi-scale feature extraction and the combination of frequency domain, spatial, and channel attention. To address these issues, this paper proposes a multi-scale and multi-attention model based on Res2Net. Our proposed framework introduces an Adaptive Feature Refinement Pyramid Module (AFRPM) to enhance multi-scale feature extraction, thus improving the accuracy of traffic scene image classification. Additionally, we integrate frequency domain and spatial-channel attention mechanisms to develop recognition capabilities for complex backgrounds, objects of different scales, and local details in traffic scene images. The paper conducts the task of classifying traffic scene images using the Traffic-Net dataset. The experimental results demonstrate that our model achieves an accuracy of 96.88% on this dataset, which is an improvement of approximately 2% compared to the baseline Res2Net network. Furthermore, we validate the effectiveness of the proposed modules through ablation experiments.

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“…The effectiveness of their approach in outperforming standalone edge and cloud schemes underscored the value of dynamic decision-making in video analysis. Bulbula Kumeda et al [4] harnessed the power of Deep Vision Neural Networks (DVNN) with CNN techniques for vehicle accident and traffic classification. Their multi-level convolutional architecture achieved high accuracy, demonstrating the potential of CNN-based models in image classification tasks.…”

Section: IImentioning

confidence: 99%

A Novel Approach for Car Accident Detection and Analysis Using AIML Techniques

Gowda,

Shivanand

2023

IJRASET

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In the context of escalating concerns regarding road safety, this study introduces an automated traffic accident detection and analysis framework that utilizes surveillance footage. The approach synergistically combines the Motion Interaction Field (MIF) model and YOLO v5 algorithm to achieve accurate crash detection and precise vehicle classification. The primary objective is to develop an autonomous system for identifying and evaluating traffic events captured by security cameras. The MIF model extracts valuable insights from interactions among moving objects within images and videos, tailored explicitly for detecting damaged vehicles. Localization of damaged vehicles is achieved using the YOLO v5 algorithm, complemented by perspective transformation to enable vertical image representation. The unbiased finite impulse response (UFIR) technique, notable for its insensitivity to statistical noise data, effectively determines vehicle motion. Furthermore, this work delves into an in-depth analysis of car crashes, estimating impact speed and point of collision from an aerial perspective. This work encompasses meticulous accident clip analysis, precise car direction determination, and error identification. The outcomes underscore the potential of the proposed framework to revolutionize accident detection and analysis, contributing to heightened road safety and streamlined traffic management strategies.

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Vehicle Accident and Traffic Classification Using Deep Convolutional Neural Networks

Cited by 18 publications

References 15 publications

Vision Transformer-Based Multi-Phase Accident Detection and False Positive Mitigation

Vision Transformer-Based Multi-Phase Accident Detection and False Positive Mitigation

Res2Net-based multi-scale and multi-attention model for traffic scene image classification

A Novel Approach for Car Accident Detection and Analysis Using AIML Techniques

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